Deep learning-driven simultaneous layout decomposition and mask optimization

Wei Zhong; Shuxiang Hu; Yuzhe Ma; Haoyu Yang; Xiuyuan Ma; Bei Yu

doi:10.1109/DAC18072.2020.9218530

Deep learning-driven simultaneous layout decomposition and mask optimization

Wei Zhong^*, Shuxiang Hu^*, Yuzhe Ma, Haoyu Yang, Xiuyuan Ma^*, Bei Yu

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

10 Citations (Scopus)

Abstract

Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization process to select the best candidates for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and mask optimization (LDMO) process. In this paper, we propose a CNN based prediction and integrate it into our new high performance LDMO framework. We also develop both the layout and the decomposition sampling strategies to facilitate the network training. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms.

Original language	English
Title of host publication	2020 57th ACM/IEEE Design Automation Conference, DAC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781450367257
DOIs	https://doi.org/10.1109/DAC18072.2020.9218530
Publication status	Published - Jul 2020
Externally published	Yes
Event	57th ACM/IEEE Design Automation Conference, DAC 2020 - Virtual, San Francisco, United States Duration: 20 Jul 2020 → 24 Jul 2020

Publication series

Name	Proceedings - Design Automation Conference
Volume	2020-July
ISSN (Print)	0738-100X

Conference

Conference	57th ACM/IEEE Design Automation Conference, DAC 2020
Country/Territory	United States
City	Virtual, San Francisco
Period	20/07/20 → 24/07/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/DAC18072.2020.9218530

Cite this

Zhong, W., Hu, S., Ma, Y., Yang, H., Ma, X., & Yu, B. (2020). Deep learning-driven simultaneous layout decomposition and mask optimization. In 2020 57th ACM/IEEE Design Automation Conference, DAC 2020 Article 9218530 (Proceedings - Design Automation Conference; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DAC18072.2020.9218530

@inproceedings{8d4c068394da43cd97a8b110d12b546a,

title = "Deep learning-driven simultaneous layout decomposition and mask optimization",

abstract = "Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization process to select the best candidates for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and mask optimization (LDMO) process. In this paper, we propose a CNN based prediction and integrate it into our new high performance LDMO framework. We also develop both the layout and the decomposition sampling strategies to facilitate the network training. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms.",

author = "Wei Zhong and Shuxiang Hu and Yuzhe Ma and Haoyu Yang and Xiuyuan Ma and Bei Yu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 57th ACM/IEEE Design Automation Conference, DAC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",

year = "2020",

month = jul,

doi = "10.1109/DAC18072.2020.9218530",

language = "English",

series = "Proceedings - Design Automation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2020 57th ACM/IEEE Design Automation Conference, DAC 2020",

}

Zhong, W, Hu, S, Ma, Y, Yang, H, Ma, X & Yu, B 2020, Deep learning-driven simultaneous layout decomposition and mask optimization. in 2020 57th ACM/IEEE Design Automation Conference, DAC 2020., 9218530, Proceedings - Design Automation Conference, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., 57th ACM/IEEE Design Automation Conference, DAC 2020, Virtual, San Francisco, United States, 20/07/20. https://doi.org/10.1109/DAC18072.2020.9218530

Deep learning-driven simultaneous layout decomposition and mask optimization. / Zhong, Wei; Hu, Shuxiang; Ma, Yuzhe et al.
2020 57th ACM/IEEE Design Automation Conference, DAC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9218530 (Proceedings - Design Automation Conference; Vol. 2020-July).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Deep learning-driven simultaneous layout decomposition and mask optimization

AU - Zhong, Wei

AU - Hu, Shuxiang

AU - Ma, Yuzhe

AU - Yang, Haoyu

AU - Ma, Xiuyuan

AU - Yu, Bei

PY - 2020/7

Y1 - 2020/7

N2 - Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization process to select the best candidates for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and mask optimization (LDMO) process. In this paper, we propose a CNN based prediction and integrate it into our new high performance LDMO framework. We also develop both the layout and the decomposition sampling strategies to facilitate the network training. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms.

AB - Combining multiple pattern lithography (MPL) and optical proximity correlation (OPC) pushes the limit of 193nm wavelength lithography to go further. Considering that layout decomposition may generate plenty of solutions with diverse printabilities, relying on conventional mask optimization process to select the best candidates for manufacturing is computationally expensive. Therefore, an accurate and efficient printability estimation is crucial and can significantly accelerate the layout decomposition and mask optimization (LDMO) process. In this paper, we propose a CNN based prediction and integrate it into our new high performance LDMO framework. We also develop both the layout and the decomposition sampling strategies to facilitate the network training. The experimental results demonstrate the effectiveness and the efficiency of the proposed algorithms.

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000628528400042

UR - https://www.scopus.com/pages/publications/85093920438

U2 - 10.1109/DAC18072.2020.9218530

DO - 10.1109/DAC18072.2020.9218530

M3 - Conference Paper published in a book

T3 - Proceedings - Design Automation Conference

BT - 2020 57th ACM/IEEE Design Automation Conference, DAC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 57th ACM/IEEE Design Automation Conference, DAC 2020

Y2 - 20 July 2020 through 24 July 2020

ER -

Deep learning-driven simultaneous layout decomposition and mask optimization

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this